A well-designed meta-analysis can provide valuable information for researchers, policy-makers, or data analysts in general. These users face an overwhelming amount of information, even in narrow areas of interest. In response, search engines designed to send alerts are frequently employed on large volumes of information. However, there are many critical caveats in performing and interpreting such large amount of information, and thus many ways in which meta-analyses can yield misleading information. To further reduce information overload, users may only want to be alerted when new trends emerge about an entity.
Searching information about entities (i.e. people, locations, organizations) in a large amount of documents, including sources such as a network, may often be ambiguous, which may lead to imprecise text processing functions, imprecise association of features during a knowledge extraction, and, thus, imprecise data analysis. Therefore, alerts based on keywords may be problematic because references to named entities are ambiguous and many alerts that are not on topic may be provided in the search results. In addition, people may not want to get alerted on everything related to an entity, but only when new knowledge (new information) about an entity is available.
Keyword search may not solve these problems as it is not easy to do that kind of filtration.
Therefore, there is still a need for tailored alerts following certain criteria to reduce results with misleading information or false positives, to increase the efficiency of monitoring, allowing for broadened universe of alerts.